Hybrid nonlinear autoregressive neural networks for permanent-magnet linear synchronous motor identification
动态驱动神经网络辨识永磁直线同步电动机模型

The modeling of permanent-magnet linear synchronous motor is crucial to the control, static and dynamic characters analysis for the system. The model of permanent-magnet linear synchronous motor is presented in this paper by using neural networks of the nonlinear autoregressive with exogenous inputs. For the same cost function, residual signal analysis is employed to identify motor's order automatically. Some shortages of back-propagation algorithm are considered, so NDEKF (node-decoupled extended Kalman filter) is applied to train networks. Finally, experiment results show that the hybrid neural networks of the nonlinear autoregressive with exogenous inputs can identify object's order precisely, and the output of networks is very close to the experimental result. In the experiment, the performance of NDEKF is often superior to that of BP, such as it requires significantly fewer presentations of training data and shorter training time than BP does, and has the better generalization ability.